Model Context Protocol (MCP) is a new open standard (introduced by Anthropic in late 2024) that unifies how AI systems like large language models (LLMs) connect to external tools, data, and services.
In MCP’s client-server architecture, an AI host (for example, a chat app or IDE) runs one or more MCP clients, each of which connects to an MCP server that provides specific functionality. An MCP server is simply a program (often a Node/TypeScript/Express service for JS developers) that registers “tools” and “resources” that the AI can invoke . These tools might let the AI read files, query a database, call an API, or perform any domain-specific action. Each action is mediated by the MCP protocol (a JSON-RPC 2.0–based interface over stdio or HTTP/SSE) so that the AI and server communicate in a standardized way .
MCP Architecture – an AI host on the user’s device runs one or more MCP clients, each connected to an MCP server. The server implements tools (functions) or data queries, often calling external APIs, and responds over the MCP protocol
This model solves the “N×M” connector problem: before MCP, developers had to write custom bridge code for each AI-tool pair, leading to many one-off integrations. MCP provides a universal interface for the AI to
discover and use tools . In practice, popular AI platforms (ChatGPT, Claude, etc.) and agent frameworks create an MCP client under the hood, so your JavaScript code can focus on exposing tools or
calling them without worrying about AI prompts.
Why Use MCP Servers? (Benefits & Use Cases)
- Standardized integrations. MCP is an open, vendor-neutral protocol (similar in spirit to OpenAPI but for AI tools.). It lets any AI client talk to any MCP server with no proprietary plumbing. Anthropic’s docs note that MCP ships with SDKs in many languages (TypeScript, Python, C#, Java, etc.) and reference implementations for GitHub, Slack, Google Drive, databases, browser automation, and more. This ecosystem means you can often reuse or adapt an existing MCP server rather than reinvent one from scratch.
- Secure, user-mediated access. By design, MCP interactions require explicit user approval. For local servers, the AI must ask the user before performing any action, ensuring you stay in control. For remote servers, MCP supports standard OAuth flows so end users can grant limited access tokens without sharing credentials. This combination of explicit approval and scoped tokens helps mitigate risks – for example, Cloudflare’s blog notes that MCP “uses OAuth” under the hood so users grant access safely.
- Immediate new capabilities for AI. Connecting an MCP server can dramatically boost an AI’s abilities. For instance, an MCP server exposing your database enables natural-language queries to that database (use cases like “AI2SQL”). In software development, MCP is already powering code-assistant tools: IDEs like VS Code, GitHub Copilot, and platforms like Replit or Sourcegraph connect MCP servers that let the AI read your codebase, search your repo, or manipulate files in real-time. In short, you can turn your AI assistant into an agent that takes actions (sending emails, updating spreadsheets, deploying code, etc.) instead of just answering questions.
- Wide adoption and momentum. Major AI providers (OpenAI, Google DeepMind, Microsoft) have embraced MCP. For example, in 2025 OpenAI announced MCP support in ChatGPT and their Agents SDK, and Google confirmed MCP support in its Gemini models. This broad industry adoption means MCP is likely to be supported everywhere agents run, so building on it is future-friendly. (One reporter even called MCP the “USB-C for AI” uniting rivals.)
- Customizable to your needs. Because the protocol is open-source and extensible, you can write your own MCP server in JavaScript to connect any data source or tool to AI. This is huge for specialized domains: for example, you could build an MCP server that interfaces with your company’s ERP system or a proprietary API, without having to wait for a vendor plugin.
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